Open AccessProceedings Article
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.
Xun Huang,Serge Belongie +1 more
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In this article, adaptive instance normalization (AdaIN) is proposed to align the mean and variance of the content features with those of the style features, which enables arbitrary style transfer in real-time.Abstract:
Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.read more
Citations
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Generative Modelling of Semantic Segmentation Data in the Fashion Domain
TL;DR: The proposed method to generatively model the joint distribution of images and corresponding semantic segmentation masks using generative adversarial networks produces samples that are coherent and plausible with semantic segmentations masks that closely match the semantics in the image.
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A Method for Arbitrary Instance Style Transfer
TL;DR: A topologically inspired algorithm called Forward Stretching is proposed to tackle the problem of transferring style to an instance in an arbitrary shape by transforming an instance into a tensor representation, which allows us to transfer style to this instance itself directly.
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Semi-Supervised Eye Makeup Transfer by Swapping Learned Representation
Feida Zhu,Hongji Cao,Zunlei Feng,Yongqiang Zhang,Wenbin Luo,Hucheng Zhou,Mingli Song,Kai-Kuang Ma +7 more
TL;DR: An autoencoder structure to transfer the eye makeup from an arbitrary reference image to a source image realistically and faithfully using both synthetic paired data and unpaired data in a semi-supervised way is introduced.
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Neural Comic Style Transfer: Case Study
Maciej Pesko,Tomasz Trzcinski +1 more
TL;DR: A comparison of how state-of-the-art style transfer methods cope with transferring various comic styles on different images and the results of a survey conducted on over 100 people that aims at validating the evaluation results in a real-life application of comic style transfer.
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Unpaired Image Translation via Adaptive Convolution-based Normalization.
TL;DR: The qualitative and quantitative experiments demonstrate the superiority of the proposed advanced normalization technique based on adaptive convolution (AdaCoN) against various existing approaches that inject the style into the content.
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